Architecture search concepts in Computer Vision

Introduction

Architecture search helps find the best design for a machine learning model automatically. It saves time and effort compared to guessing the model structure.

When you want to build a new image recognition model but don't know the best layer setup.

When you want to improve an existing model's accuracy without manual trial and error.

When you have many possible model designs and want to find the fastest one that works well.

When you want to automate model design to save time for other tasks.

When you want to explore new model ideas that humans might not think of.

Syntax

Computer Vision

Define a search space of possible model parts
Choose a search strategy (e.g., random, evolutionary, reinforcement learning)
Run the search to test different models
Select the best model found

The search space is like a menu of model options to try.

The search strategy decides how to pick which models to test next.

Examples

This tries random models with 2 to 5 layers and picks the best one.

Computer Vision

Search space: number of layers = 2 to 5
Search strategy: random sampling
Run search for 10 models
Pick model with highest accuracy

This uses evolution ideas to improve models over time.

Computer Vision

Search space: convolution filter sizes = 3x3 or 5x5
Search strategy: evolutionary algorithm
Run search for 20 generations
Select model with best validation score

Sample Model

This code tries 10 random models with different layers and filter sizes. It prints each model's accuracy and shows the best one found.

Computer Vision

import random

# Define search space options
layer_options = [2, 3, 4]
filter_sizes = [3, 5]

# Dummy function to simulate model accuracy
# Higher layers and bigger filters give better accuracy here

def evaluate_model(layers, filter_size):
    base_accuracy = 0.7
    accuracy = base_accuracy + 0.05 * (layers - 2) + 0.03 * (filter_size - 3)
    noise = random.uniform(-0.01, 0.01)
    return accuracy + noise

# Simple random search
best_model = None
best_accuracy = 0

for _ in range(10):
    layers = random.choice(layer_options)
    filter_size = random.choice(filter_sizes)
    acc = evaluate_model(layers, filter_size)
    print(f"Tested model with {layers} layers and {filter_size}x{filter_size} filters: accuracy={acc:.3f}")
    if acc > best_accuracy:
        best_accuracy = acc
        best_model = (layers, filter_size)

print(f"\nBest model found: {best_model[0]} layers, {best_model[1]}x{best_model[1]} filters with accuracy {best_accuracy:.3f}")

Important Notes

Architecture search can take a lot of time if the search space is big.

Using a smart search strategy helps find good models faster.

Always test the final model on new data to check real performance.

Summary

Architecture search finds the best model design automatically.

It tries different model setups from a defined search space.

Choosing a good search strategy speeds up finding a good model.